Polyphonic note and instrument tracking using linear dynamical systems
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In this paper, a system for automatic transcription of multiple-instrument polyphonic music is proposed, which supports tracking multiple concurrent notes using linear dynamical systems (LDS). The system is based on a spectrogram factorisation model which extends probabilistic latent component analysis (PLCA), and supports the detection of multiple pitches, instrument contributions, and pitch deviations. In order to jointly track multiple concurrent pitches, the use of LDS as prior to the PLCA model is proposed. LDS parameters are learned in a training stage using score-informed transcriptions; for LDS inference, online and offline variants are evaluated. The MAPS piano music dataset and the Bach10 multi-instrument dataset are used for note tracking experiments, with the latter dataset also being evaluated with respect to instrument assignment performance. Results show that the proposed LDS-based method can successfully track multiple concurrent notes, leading to an improvement of over 3% in terms of note-based F-measure for both datasets over benchmark note tracking approaches.